{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing Website Topics (Claude + Firecrawl + E2B)\n",
    "\n",
    "**Powered by [Claude 3.5 Sonnet](https://www.anthropic.com/news/claude-3-5-sonnet), [Firecrawl](https://www.firecrawl.dev/), and [Code Interpreter SDK](https://github.com/e2b-dev/code-interpreter) by [E2B](https://e2b.dev/docs)**\n",
    "\n",
    "Scrape a website with Firecrawl and then plot the most common topics using Claude and Code Interpreter\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: e2b_code_interpreter==1.0.0 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (1.0.0)\n",
      "Requirement already satisfied: anthropic==0.35.0 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (0.35.0)\n",
      "Requirement already satisfied: firecrawl-py==0.0.16 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (0.0.16)\n",
      "Requirement already satisfied: python-dotenv==1.0.1 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (1.0.1)\n",
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      "Requirement already satisfied: e2b<2.0.0,>=1.0.0 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from e2b_code_interpreter==1.0.0) (1.0.1)\n",
      "Requirement already satisfied: httpx<0.28.0,>=0.20.0 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from e2b_code_interpreter==1.0.0) (0.27.0)\n",
      "Requirement already satisfied: anyio<5,>=3.5.0 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from anthropic==0.35.0) (3.7.1)\n",
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      "Requirement already satisfied: pydantic<3,>=1.9.0 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from anthropic==0.35.0) (2.9.1)\n",
      "Requirement already satisfied: sniffio in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from anthropic==0.35.0) (1.3.0)\n",
      "Requirement already satisfied: tokenizers>=0.13.0 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from anthropic==0.35.0) (0.20.0)\n",
      "Requirement already satisfied: typing-extensions<5,>=4.7 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from anthropic==0.35.0) (4.12.2)\n",
      "Requirement already satisfied: requests in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from firecrawl-py==0.0.16) (2.31.0)\n",
      "Requirement already satisfied: idna>=2.8 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from anyio<5,>=3.5.0->anthropic==0.35.0) (3.6)\n",
      "Requirement already satisfied: httpcore<2.0.0,>=1.0.5 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from e2b<2.0.0,>=1.0.0->e2b_code_interpreter==1.0.0) (1.0.5)\n",
      "Requirement already satisfied: packaging<25.0,>=24.1 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from e2b<2.0.0,>=1.0.0->e2b_code_interpreter==1.0.0) (24.1)\n",
      "Requirement already satisfied: protobuf<6.0.0,>=3.20.0 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from e2b<2.0.0,>=1.0.0->e2b_code_interpreter==1.0.0) (4.24.3)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from e2b<2.0.0,>=1.0.0->e2b_code_interpreter==1.0.0) (2.8.2)\n",
      "Requirement already satisfied: certifi in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from httpx<0.28.0,>=0.20.0->e2b_code_interpreter==1.0.0) (2024.8.30)\n",
      "Requirement already satisfied: h11<0.15,>=0.13 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from httpcore<2.0.0,>=1.0.5->e2b<2.0.0,>=1.0.0->e2b_code_interpreter==1.0.0) (0.14.0)\n",
      "Requirement already satisfied: annotated-types>=0.6.0 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from pydantic<3,>=1.9.0->anthropic==0.35.0) (0.7.0)\n",
      "Requirement already satisfied: pydantic-core==2.23.3 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from pydantic<3,>=1.9.0->anthropic==0.35.0) (2.23.3)\n",
      "Requirement already satisfied: huggingface-hub<1.0,>=0.16.4 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from tokenizers>=0.13.0->anthropic==0.35.0) (0.25.1)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from requests->firecrawl-py==0.0.16) (3.3.2)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from requests->firecrawl-py==0.0.16) (2.2.1)\n",
      "Requirement already satisfied: filelock in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers>=0.13.0->anthropic==0.35.0) (3.15.4)\n",
      "Requirement already satisfied: fsspec>=2023.5.0 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers>=0.13.0->anthropic==0.35.0) (2023.6.0)\n",
      "Requirement already satisfied: pyyaml>=5.1 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers>=0.13.0->anthropic==0.35.0) (6.0.1)\n",
      "Requirement already satisfied: tqdm>=4.42.1 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers>=0.13.0->anthropic==0.35.0) (4.66.2)\n",
      "Requirement already satisfied: six>=1.5 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from python-dateutil>=2.8.2->e2b<2.0.0,>=1.0.0->e2b_code_interpreter==1.0.0) (1.16.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install e2b_code_interpreter==1.0.0 anthropic==0.35.0 firecrawl-py==0.0.16 python-dotenv==1.0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from firecrawl import FirecrawlApp\n",
    "from dotenv import load_dotenv\n",
    "load_dotenv()\n",
    "\n",
    "# TODO: Get your Anthropic API key from https://anthropic.com\n",
    "anthropic_api_key = os.getenv(\"ANTHROPIC_API_KEY\")\n",
    "# TODO: Get your Firecrawl API key from https://www.firecrawl.dev\n",
    "firecrawl_api_key = os.getenv(\"FIRECRAWL_API_KEY\")\n",
    "# TODO: Get your E2B API key from https://e2b.dev/docs\n",
    "e2b_api_key = os.getenv(\"E2B_API_KEY\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the FirecrawlApp with your API key\n",
    "app = FirecrawlApp(api_key=firecrawl_api_key)\n",
    "\n",
    "# Crawl a website\n",
    "crawl_url = 'https://python.langchain.com/docs/introduction/'\n",
    "params = {\n",
    "    'crawlerOptions': {\n",
    "        'limit': 5\n",
    "    }\n",
    "}\n",
    "crawl_result = app.crawl_url(crawl_url, params=params)\n",
    "cleaned_crawl_result = []\n",
    "if crawl_result is not None:\n",
    "    # Convert crawl results to JSON format, excluding 'content' field from each entry\n",
    "    cleaned_crawl_result = [{k: v for k, v in entry.items() if k != 'content'} for entry in crawl_result]\n",
    "else:\n",
    "    print(\"No data returned from crawl.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MODEL_NAME = \"claude-3-5-sonnet-20240620\"\n",
    "MODEL_NAME = \"claude-3-5-sonnet-20241022\"\n",
    "\n",
    "SYSTEM_PROMPT = \"\"\"\n",
    "## your job & context\n",
    "you are a python data scientist. you are given tasks to complete and you run python code to solve them.\n",
    "- the python code runs in jupyter notebook.\n",
    "- every time you call `execute_python` tool, the python code is executed in a separate cell. it's okay to multiple calls to `execute_python`.\n",
    "- display visualizations using matplotlib or any other visualization library directly in the notebook. don't worry about saving the visualizations to a file.\n",
    "- you have access to the internet and can make api requests.\n",
    "- you also have access to the filesystem and can read/write files.\n",
    "- you can install any pip package (if it exists) if you need to but the usual packages for data analysis are already preinstalled.\n",
    "- you can run any python code you want, everything is running in a secure sandbox environment.\n",
    "\n",
    "## style guide\n",
    "tool response values that have text inside \"[]\"  mean that a visual element got rended in the notebook. for example:\n",
    "- \"[chart]\" means that a chart was generated in the notebook.\n",
    "\"\"\"\n",
    "\n",
    "tools = [\n",
    "    {\n",
    "        \"name\": \"execute_python\",\n",
    "        \"description\": \"Execute python code in a Jupyter notebook cell and returns any result, stdout, stderr, display_data, and error.\",\n",
    "        \"input_schema\": {\n",
    "            \"type\": \"object\",\n",
    "            \"properties\": {\n",
    "                \"code\": {\n",
    "                    \"type\": \"string\",\n",
    "                    \"description\": \"The python code to execute in a single cell.\"\n",
    "                }\n",
    "            },\n",
    "            \"required\": [\"code\"]\n",
    "        }\n",
    "    }\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def code_interpret(e2b_code_interpreter, code):\n",
    "  print(\"Running code interpreter...\")\n",
    "  exec = e2b_code_interpreter.run_code(\n",
    "    code,\n",
    "    on_stderr=lambda stderr: print(\"[Code Interpreter]\", stderr),\n",
    "    on_stdout=lambda stdout: print(\"[Code Interpreter]\", stdout),\n",
    "    # You can also stream code execution results\n",
    "    # on_result=...\n",
    "    timeout=600\n",
    "  )\n",
    "\n",
    "  if exec.error:\n",
    "    print(\"[Code Interpreter ERROR]\", exec.error)\n",
    "  else:\n",
    "    return exec.results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from anthropic import Anthropic\n",
    "client = Anthropic(\n",
    "    api_key=anthropic_api_key,\n",
    ")\n",
    "\n",
    "def process_tool_call(e2b_code_interpreter, tool_name, tool_input):\n",
    "    if tool_name == \"execute_python\":\n",
    "        return code_interpret(e2b_code_interpreter, tool_input[\"code\"])\n",
    "    return []\n",
    "\n",
    "def chat_with_claude(e2b_code_interpreter, user_message):\n",
    "    print(f\"\\n{'='*50}\\nUser Message: {user_message}\\n{'='*50}\")\n",
    "\n",
    "    message = client.messages.create(\n",
    "        model=MODEL_NAME,\n",
    "        system=SYSTEM_PROMPT,\n",
    "        messages=[{\"role\": \"user\", \"content\": user_message}],\n",
    "        max_tokens=4096,\n",
    "        tools=tools,\n",
    "    )\n",
    "\n",
    "    print(f\"\\nInitial Response:\")\n",
    "    print(f\"Stop Reason: {message.stop_reason}\")\n",
    "    print(f\"Content: {message.content}\")\n",
    "\n",
    "    if message.stop_reason == \"tool_use\":\n",
    "        tool_use = next(block for block in message.content if block.type == \"tool_use\")\n",
    "        tool_name = tool_use.name\n",
    "        tool_input = tool_use.input\n",
    "\n",
    "        print(f\"\\nTool Used: {tool_name}\")\n",
    "        print(f\"Tool Input: {tool_input}\")\n",
    "\n",
    "        code_interpreter_results = process_tool_call(e2b_code_interpreter, tool_name, tool_input)\n",
    "\n",
    "        print(f\"Tool Result: {code_interpreter_results}\")\n",
    "        return code_interpreter_results\n",
    "     \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "==================================================\n",
      "User Message: Use python to identify the most common topics in the crawl results. For each topic, count the number of times it appears in the crawl results and plot them. Here is the crawl results: [{'markdown': '[Skip to main content](#__docusaurus_skipToContent_fallback)\\n\\n[![🦜️🔗 LangChain](https://python.langchain.com/img/brand/wordmark.png)](/)\\n[Integrations](/docs/integrations/providers/)\\n[API Reference](https://python.langchain.com/api_reference/)\\n\\n[More](#)\\n\\n*   [Contributing](/docs/contributing/)\\n    \\n*   [People](/docs/people/)\\n    \\n*   [Error reference](/docs/troubleshooting/errors/)\\n    \\n*   * * *\\n    \\n*   [LangSmith](https://docs.smith.langchain.com)\\n    \\n*   [LangGraph](https://langchain-ai.github.io/langgraph/)\\n    \\n*   [LangChain Hub](https://smith.langchain.com/hub)\\n    \\n*   [LangChain JS/TS](https://js.langchain.com)\\n    \\n\\n[v0.3](#)\\n\\n*   [v0.3](/docs/introduction/)\\n    \\n*   [v0.2](https://python.langchain.com/v0.2/docs/introduction)\\n    \\n*   [v0.1](https://python.langchain.com/v0.1/docs/get_started/introduction)\\n    \\n\\n[💬](https://chat.langchain.com)\\n[](https://github.com/langchain-ai/langchain)\\n\\nSearchK\\n\\n*   [Introduction](/docs/introduction/)\\n    \n",
      "==================================================\n",
      "\n",
      "Initial Response:\n",
      "Stop Reason: tool_use\n",
      "Content: [TextBlock(text=\"I'll help you analyze the topics in the crawl results using Python. We'll use natural language processing techniques to identify and count the topics, then create a visualization. Let's use the `collections` module for counting and `matplotlib` for plotting.\\n\\nFirst, let's process the text and identify common topics:\", type='text'), ToolUseBlock(id='toolu_01Xcntk7CvMTzBaMV4CfhYPh', input={'code': 'import re\\nfrom collections import Counter\\nimport matplotlib.pyplot as plt\\n\\n# Extract the text from the markdown\\ntext = \\'\\'\\'[Skip to main content](#__docusaurus_skipToContent_fallback)\\n\\n[![🦜️🔗 LangChain](https://python.langchain.com/img/brand/wordmark.png)](/)\\\\n[Integrations](/docs/integrations/providers/)\\\\n[API Reference](https://python.langchain.com/api_reference/)\\\\n\\\\n[More](#)\\\\n\\\\n*   [Contributing](/docs/contributing/)\\\\n    \\\\n*   [People](/docs/people/)\\\\n    \\\\n*   [Error reference](/docs/troubleshooting/errors/)\\\\n    \\\\n*   * * *\\\\n    \\\\n*   [LangSmith](https://docs.smith.langchain.com)\\\\n    \\\\n*   [LangGraph](https://langchain-ai.github.io/langgraph/)\\\\n    \\\\n*   [LangChain Hub](https://smith.langchain.com/hub)\\\\n    \\\\n*   [LangChain JS/TS](https://js.langchain.com)\\\\n\\'\\'\\'\\n\\n# Extract words that look like topics (mainly looking for LangChain related terms and menu items)\\ntopics = re.findall(r\\'\\\\[(.*?)\\\\]\\', text)  # Extract text between square brackets\\ntopics = [t for t in topics if not t.startswith(\\'http\\') and t != \\'More\\' and t != \\'Skip to main content\\']\\n\\n# Count the occurrences\\ntopic_counts = Counter(topics)\\n\\n# Sort by count in descending order\\nsorted_topics = dict(sorted(topic_counts.items(), key=lambda x: x[1], reverse=True))\\n\\n# Create a bar plot\\nplt.figure(figsize=(12, 6))\\nplt.bar(sorted_topics.keys(), sorted_topics.values())\\nplt.xticks(rotation=45, ha=\\'right\\')\\nplt.title(\\'Frequency of Topics in Crawl Results\\')\\nplt.xlabel(\\'Topics\\')\\nplt.ylabel(\\'Count\\')\\nplt.tight_layout()\\nplt.show()\\n\\n# Print the counts\\nfor topic, count in sorted_topics.items():\\n    print(f\"{topic}: {count}\")'}, name='execute_python', type='tool_use')]\n",
      "\n",
      "Tool Used: execute_python\n",
      "Tool Input: {'code': 'import re\\nfrom collections import Counter\\nimport matplotlib.pyplot as plt\\n\\n# Extract the text from the markdown\\ntext = \\'\\'\\'[Skip to main content](#__docusaurus_skipToContent_fallback)\\n\\n[![🦜️🔗 LangChain](https://python.langchain.com/img/brand/wordmark.png)](/)\\\\n[Integrations](/docs/integrations/providers/)\\\\n[API Reference](https://python.langchain.com/api_reference/)\\\\n\\\\n[More](#)\\\\n\\\\n*   [Contributing](/docs/contributing/)\\\\n    \\\\n*   [People](/docs/people/)\\\\n    \\\\n*   [Error reference](/docs/troubleshooting/errors/)\\\\n    \\\\n*   * * *\\\\n    \\\\n*   [LangSmith](https://docs.smith.langchain.com)\\\\n    \\\\n*   [LangGraph](https://langchain-ai.github.io/langgraph/)\\\\n    \\\\n*   [LangChain Hub](https://smith.langchain.com/hub)\\\\n    \\\\n*   [LangChain JS/TS](https://js.langchain.com)\\\\n\\'\\'\\'\\n\\n# Extract words that look like topics (mainly looking for LangChain related terms and menu items)\\ntopics = re.findall(r\\'\\\\[(.*?)\\\\]\\', text)  # Extract text between square brackets\\ntopics = [t for t in topics if not t.startswith(\\'http\\') and t != \\'More\\' and t != \\'Skip to main content\\']\\n\\n# Count the occurrences\\ntopic_counts = Counter(topics)\\n\\n# Sort by count in descending order\\nsorted_topics = dict(sorted(topic_counts.items(), key=lambda x: x[1], reverse=True))\\n\\n# Create a bar plot\\nplt.figure(figsize=(12, 6))\\nplt.bar(sorted_topics.keys(), sorted_topics.values())\\nplt.xticks(rotation=45, ha=\\'right\\')\\nplt.title(\\'Frequency of Topics in Crawl Results\\')\\nplt.xlabel(\\'Topics\\')\\nplt.ylabel(\\'Count\\')\\nplt.tight_layout()\\nplt.show()\\n\\n# Print the counts\\nfor topic, count in sorted_topics.items():\\n    print(f\"{topic}: {count}\")'}\n",
      "Running code interpreter...\n",
      "[Code Interpreter] /tmp/ipykernel_866/2811069906.py:27: UserWarning: Glyph 129436 (\\N{PARROT}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/tmp/ipykernel_866/2811069906.py:27: UserWarning: Glyph 128279 (\\N{LINK SYMBOL}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/usr/local/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 129436 (\\N{PARROT}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/usr/local/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 128279 (\\N{LINK SYMBOL}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "\n",
      "[Code Interpreter] ![🦜️🔗 LangChain: 1\n",
      "Integrations: 1\n",
      "API Reference: 1\n",
      "Contributing: 1\n",
      "People: 1\n",
      "Error reference: 1\n",
      "LangSmith: 1\n",
      "LangGraph: 1\n",
      "LangChain Hub: 1\n",
      "LangChain JS/TS: 1\n",
      "\n",
      "Tool Result: [Result(<Figure size 1200x600 with 1 Axes>)]\n",
      "[Result(<Figure size 1200x600 with 1 Axes>)]\n"
     ]
    }
   ],
   "source": [
    "from e2b_code_interpreter import Sandbox\n",
    "\n",
    "with Sandbox(api_key=e2b_api_key) as code_interpreter:\n",
    "  code_interpreter_results = chat_with_claude(\n",
    "    code_interpreter,\n",
    "    \"Use python to identify the most common topics in the crawl results. For each topic, count the number of times it appears in the crawl results and plot them. Here is the crawl results: \" + str(cleaned_crawl_result)[:1024],\n",
    "  )\n",
    "print(code_interpreter_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Result(<Figure size 1200x600 with 1 Axes>)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Result(<Figure size 1200x600 with 1 Axes>)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = code_interpreter_results[0]\n",
    "print(result)\n",
    "\n",
    "result"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
